Development of an algorithm for fast corner points detection

A method for detecting corner points in digital images is presented. The method is distinguished by high stability and efficiency compared with many method for detecting corner points developed earlier. The stability of corner detection is especially important in computer vision tasks connected with...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of computer & systems sciences international Jg. 53; H. 3; S. 392 - 401
1. Verfasser: Verkeenko, M. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.05.2014
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Artificial intelligence
Computer vision
Control
Corner detection
Corners
Decision trees
Digital
Digital imaging
Engineering
Image detection
Machine learning
Mathematical models
Mechatronics
Methods
Navigation systems
Pattern recognition
Pattern Recognition and Image Processing
Real time
Robotics
Robots
Sensors
Stability
Studies
Systems science
Unmanned aerial vehicles
Vision systems
Edge Image
International Space Station
Corner Point
Interest Point
System Science International
ISSN:1064-2307, 1555-6530
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A method for detecting corner points in digital images is presented. The method is distinguished by high stability and efficiency compared with many method for detecting corner points developed earlier. The stability of corner detection is especially important in computer vision tasks connected with matching images of the same object, recovering digital surface models based on a set of images, and tracking objects. The overwhelming majority of algorithms detect equally well both correct corners and excessive points not corresponding to real corners of objects. The presented algorithm does have this disadvantage, and it can be used in frame-to-frame processing video in real time, e.g. in navigation systems of mobile robots and unmanned aerial vehicles. In addition, the proposed algorithm may be adapted to any data set since it is based on the machine learning method. The advantages of the developed method are demonstrated by an example of detection of corners in images of a typical hangar and in images with the international space station.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:1064-2307
1555-6530
DOI:10.1134/S1064230714030162